The NYU-Depth-v2 dataset contains approximately \(120K\)  RGB-D images recorded from 464 indoor scenes. We extract the raw RGB frames from the original videos and reconstruct sparse 3D point clouds using the COLMAP [1]}, [2]} structure-from-motion software. COLMAP is also used to extract the camera poses for multi-view stereo methods. The 3D points are back-projected to each input view to obtain a sparse set of depth values. We use 60K images for training and 654 images from the official test set for evaluating the methods. For KITTI, we utilize 85K images for training, 1000 images for validation and 1000 images for testing on the KITTI depth completion benchmark [3]}.
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